Cross-Comparison of EMG-to-Force Methods for Multi-DoF Finger Force Prediction Using One-DoF Training

Abstract

Surface electromyography (sEMG) signal is one of the widely applied biological signals in the research field of the force intention prediction. However, due to the severe cross-talk issue of sEMG signals during fine hand contractions, few studies have related sEMG to multiple degree-of-freedom (DoF) force prediction of individual fingers simultanously. Accordingly, this study proposed methods mainly based on neural networks: Convolutional neural Network (CNN) and Recurrent Neural Network (RNN) to achieve better prediction results. Several improvements on traditional methods are also proposed in this article such as: Common Spatial Pattern (CSP), Softmax function and several new channel selection standards to solve the cross-talk issues for the estimation of EMG-force during multiple finger contractions. High-density sEMG signals of forearm extensor muscles were obtained, and experimental data from seven able-bodied subjects were analyzed. Subjects produced 1-DoF and Multi-DoF forces up to 30% maximum voluntary contraction (MVC). Then, the root-mean-square values of sEMG were related to joint force. To realize a better practical use, the EMG-to-force models were trained with minimal numbers of trials (using 1-DoF trials only), then assessed on multi-DoF trials. Our results showed that the proposed modifications on traditional method also made an improvement on the prediction results. Our findings suggest that Multi-DoF control for individual fingers with minimal training procedure (using 1-DoF trials only) may be feasible for practical use. Furthermore, methods based on neural networks greatly outperform traditional methods and the combination of CNN and LSTM showed the best performance.

Keywords

• Neural networks
• EMG-force prediction
• EMG signal processing